Lambda architectures for graph-powered applications
When dealing with scalable, graph-powered, data-driven applications, the design of Lambda architectures is also reflected in the separation of functionalities between two crucial components of the analytical pipeline:
- The graph processing engine executes computations on the graph structure in order to extract features (such as embeddings), compute statistics (such as degree distributions, the number of edges, and cliques), compute metrics and key performance indicators (KPIs) (such as centrality measures and clustering coefficients), and identify relevant subgraphs (for example, communities) that often require OLAP.
- The graph querying engine allows us to persist network data (usually done via a graph database) and provides fast information retrieval and efficient querying and graph traversal (usually via graph querying languages). All of the information is already persisted in some data storage (that may or may not...
